Diagnostics of rotating components are a maturing field and the tachometer plays an important role in the quality of vibration based diagnostics. Various studies have disclosed a number of analysis techniques, such as synchronous analyses (primarily for shafts and gears) and non-synchronous analyses (primarily for bearings). Synchronous analyses are typically based on the time synchronous average so as to eliminate signal components that are not synchronous with the rate of rotation of the shaft or gear, whereas non-synchronous analysis generally uses some type of demodulation and enveloping, returning energy associated with the fault frequency of the item under analysis (e.g., bearing).
Synchronous analyses of vibration signals relating to rotating equipment have used the Fourier transform or the Fast Fourier transform (FFT) (the latter being more typically employed for processing efficiencies) to provide vibration based diagnostics by measuring the magnitude and phase of vibration of components under observation (such as shafts, gears or bearings), which can be indicative of wear and failure. When using the FFT, typically one assumes that the signal under analysis is infinite in time; however, this assumption fails for real signals and a common mitigation technique is the use of a window function, such as a Hamming window (general form:
            w      ⁡              (        n        )              =          α      -              β        ⁢                                  ⁢                  cos          ⁡                      (                                          2                ⁢                π                ⁢                                                                  ⁢                n                                            N                -                1                                      )                                )or a Hanning window.
Another common assumption is that the vibration signal is stationary; however, as all rotating machines vary in their rotational rate due to changing load conditions and the limits of the feedback control bandwidth, this assumption of stationarity also commonly fails. In practice, the lack of stationarity results in “spectral smearing” of energy associated with a shaft, which in turn results in inaccurate measuring of the energy associated with a particular fault frequency. To improve the performance of vibration analysis using the FFT, Time Synchronous Averaging (the TSA, for shaft/gear analysis) and Time Synchronous Resampling (TSR) have been developed. Examples of TSA and TSR systems are shown in FIG. 1.
At a high level, the TSA resamples the vibration associated with a shaft or gear in the spatial domain such that vibration associated with each shaft order in the Fourier domain represents one frequency bin. For example, the gear mesh energy of a 37-tooth gear on a given shaft is found in the Fourier domain to be bin 38, and the second harmonic of that gear would be in bin 75 (37×2+1, (bin 1 is the DC energy)). The TSA also reduces non-synchronous vibration by 1/√(rev), where rev is the total number of shaft revolutions that constructed the TSA.
The TSR resamples (e.g., upsamples) the vibration to correct for variation in shaft speed. The apparent sample rate is the ratio of the total resampled time domain, i.e., vibration data set length divided by original data set length, multiplied by the original sample rate. For example, consider a system in which the shaft rate is such that for a given vibration sample rate, the acquisition system on average collects 800 samples per revolution. The TSR would resample the 800 samples to 1024 data points. (The value 1024 is the closest radix-2 value that is not less than 800. Radix-2 values are typically used because the simplest implementation of the FFT is based on powers of 2, i.e., radix-2 values.) If the load on the shaft decreases, the rotation rate of the shaft will increase, and the measured vibration will result in only 780 samples. Since it takes less time for the shaft to make one revolution, the number of samples will be fewer. The 780 samples are resampled to 1024 points by the TSR. If, on the other hand, the load on the shaft increases, slowing the shaft, the number of measured samples may increase to 820 samples, for example. Once again, the TSR will resample this data to 1024 points. For every revolution of the shaft, the resampled data is summed point by point. After n revolutions, each of the 1024 points of resampled vibration data is divided by n, essentially time synchronously averaging the vibration data.
TSA and TSR typically use a tachometer signal to calculate the time over which a shaft completes one revolution. As is generally known, the time taken for any shaft to complete a rotation can be calculated even if the tachometer is not associated with a given shaft. This can be calculated, for example, by taking into account the shaft ratio between the shaft with a tachometer to the shaft under analysis, then interpolating based on the known tachometer signal.
In implementation, the tachometer signal is the rising edge of a voltage trigger from the passing of a shaft key phasor (e.g. a stationary point of the shaft). The tachometer signal is then converted to time. This time is accrued for each pass of the key phasor. In an architecture where the tachometer signal is recorded using an analog to digital converter (ADC), the resolution in time of the rising edge is 1 over the sample rate of the ADC. For condition monitoring purposes, the sample rate for a high-speed shaft would be 100,000 samples per second. In another architecture, the tachometer signal inputs into a voltage comparator. When the tachometer signal crosses zero (or some low voltage offset), the comparator voltage goes high. The output of the comparator is monitored by the microcontroller using a general purpose input/output (GPIO) pin. When the microcontroller senses the GPIO pin going high, it records the time. The resolution of time on the microcontroller is typically much higher than an ADC. For example, in a system using a 12 MHz clock, the microcontroller might run at 96 MHz, but the counter for time in the microcontroller would run at 48 MHz. The tachometer resolution in time would then be 2.0822e-8 seconds.
The type of tachometer signal is dependent on the sensor type. Types of sensors typically used include, but are not limited to: 1) a Hall sensor, where there is a rising voltage associated with the passing of a ferrous target (such as a gear tooth) in front of the sensor; 2) an inductive sensor, where there is a rising voltage associated with the passing of any metallic target (such as an aluminum shaft coupling); 3) an optical sensor, where there is a rising voltage associated with the receiving of light from a reflective target on the shaft; or 4) a generator or variable reluctance sensor, where the frequency and amplitude of a sinusoidal signal is proportional to target (usually a gear) RPM, and the time of the zero crossing is taken at the transition of the sinusoid from negative to positive voltage.
In many instances, however, installation and/or use of these types of tachometer sensors may be impractical or undesirable. For example, there may be cases, such as glandless pumps, where due to heat and pressure it is impractical or infeasible to install a tachometer sensor. In other situations, such as monitoring gas turbine engines, interfacing with the existing tachometer for the power turbine or compressor turbine may change certification requirements (adding cost) or increase system cost and weight.
Therefore, there is a need to obtain information associated with tachometer signals in circumstances that prevent the use of a tachometer sensor or when interfacing with a tachometer sensor is difficult or impractical. This can be accomplished as disclosed herein through the use of smart sensors that can acquire vibration data associated with a rotating shaft, extract the shaft speed from the vibration data, and then process the data. This allows for an improved fault detection capability at a lower cost, a lower weight, and a reduced installation complexity compared to previously available techniques. Reducing cost, weight, and installation complexity will provide for the expanded application of condition monitoring, which would improve safety and reliability in industrial and transportation systems.
Spectral content of vibration, which can be used to monitor rotating components, can be abstracted from measured signals using the Fast Fourier Transform (FFT). The FFT is used in vibration based diagnostics to determine the magnitude and phase of the vibration of components (such as shafts, gears, or bearings), which can be indicative or wear and failure. Additionally, many common vibration analyses, such as residual analysis, difference analysis, or narrowband analysis, use the FFT for ideal filtering of the signal or to perform a Hilbert transform of the signal (i.e., Amplitude and Frequency Analysis).
Regardless of how vibration data is acquired, an incorrect tachometer signal reduces the effectiveness of the TSA and TSR to reduce spectral smearing, which negatively affects the ability of the vibration analysis to detect component faults. While important to all frequency signatures, the impacts are more apparent to higher frequency signatures and higher harmonics, which are often present when a component has a fault. Therefore, the techniques disclosed herein for reducing jitter in tachometer signals can improve the ability to monitor components and detect faults.